The Bestseller Palette: Data-Backed Trends in Book Cover Colors

#Introduction

Book cover design plays a crucial role in attracting readers and influencing purchasing decisions. One key aspect of cover design is color, which conveys mood, genre conventions, and marketing strategies. This study aims to analyze the color palettes of book covers across different genres in two major online book platforms: the top-selling sections of Casa del Libro and compare them to Goodreads Readers’ Favorite Books 2024.

By examining whether specific color schemes are more prevalent in certain categories, this research seeks to uncover patterns in book cover aesthetics and their possible correlation with genre expectations and consumer preferences.

To conduct this analysis, we used R for web scraping to collect cover images from both platforms and extract dominant color palettes using image processing techniques.

Libraries

rm(list = ls()) 

packages = c("RSelenium", "rvest", "magrittr", "tidyr", "dplyr", "magick", "tidyverse", "ggplot2", 
             "scales", "shiny", "writexl", "shinyjs", "shinythemes","shinyWidgets", "rsconnect")

package.check <- lapply(packages,
                        FUN = function(x){
                          if (!require(x,character.only = TRUE)){
                            install.packages(x,dependencies = TRUE)
                            library(x, character.only = TRUE)
                          }
                        }
)

Casa del Libro

Casa del Libro is a well-established Spanish bookstore chain and online platform that serves as a hub for book lovers. It provides a vast catalog of books across various genres, offering both physical and digital formats.

Categories & sub-categories

For this research, we analyzed the best-selling books on Casa del Libro across five key genres:

• Biographies

• Science Fiction

• Mystery & Thriller

• Romance

• Young Adult Fantasy

To achieve this, we first identified the paths to the broader categories that contain these subcategories. This step allows us to systematically navigate the website and efficiently scrape the relevant data.

# Categories & Subcategories
categories <- data.frame(
  category = c("Biography", "Science Fiction", "Mystery & Thriller", "Romance", 
               "Young Adult Fantasy"),
  category_xpath = c("//a[contains(text(),'No Ficción')]",
                     "//a[contains(text(),'Ficción')]", 
                     "//a[contains(text(),'Ficción')]", 
                     "//a[contains(text(),'Ficción')]", 
                     "//a[contains(text(),'Juvenil')]"),
  subcategory_xpath = c("//a[contains(text(),'Biografías')]",
                        "//a[span[contains(text(),'Novela de ciencia ficción')]]", 
                        "//a[span[contains(text(),'Novela negra')]]", 
                        "//a[span[contains(text(),'Novela romántica y erótica')]]",
                        "//a[span[contains(text(),'Fantasía')]]")
)

Selenium

We will use Selenium to automate the scraping process, enabling us to navigate Casa del Libro’s website and efficiently extract book data. To achieve this, we initialize the Selenium remote driver and navigate to the homepage, ensuring a seamless start to the data collection process.

remDr <- remoteDriver(port = 4449)
remDr$open()

[1] "Connecting to remote server"
$acceptInsecureCerts
[1] FALSE

$browserName
[1] "firefox"

$browserVersion
[1] "92.0.1"

$`moz:accessibilityChecks`
[1] FALSE

$`moz:buildID`
[1] "20210922161155"

$`moz:geckodriverVersion`
[1] "0.29.1"

$`moz:headless`
[1] FALSE

$`moz:processID`
[1] 1160

$`moz:profile`
[1] "/tmp/rust_mozprofileGnZe3u"

$`moz:shutdownTimeout`
[1] 60000

$`moz:useNonSpecCompliantPointerOrigin`
[1] FALSE

$`moz:webdriverClick`
[1] TRUE

$pageLoadStrategy
[1] "normal"

$platformName
[1] "linux"

$platformVersion
[1] "6.12.5-linuxkit"

$proxy
named list()

$setWindowRect
[1] TRUE

$strictFileInteractability
[1] FALSE

$timeouts
$timeouts$implicit
[1] 0

$timeouts$pageLoad
[1] 300000

$timeouts$script
[1] 30000


$unhandledPromptBehavior
[1] "dismiss and notify"

$webdriver.remote.sessionid
[1] "376b3b3b-9641-47e0-88bd-fdc092132e5d"

$id
[1] "376b3b3b-9641-47e0-88bd-fdc092132e5d"

#Go to Casa del Libro webpage
remDr$navigate("https://www.casadellibro.com")

Sys.sleep(5)

Privacy window

If the privacy window appears, the code utilizes tryCatch() to manage the rejection of the cookie consent banner on the Casa del Libro website. It attempts to locate the “Reject All” button using an XPath query. If the button is found, the code clicks on it to reject the cookies. If the button is not present, the script continues without interruption and outputs a message indicating that the privacy window was not displayed. This approach ensures that the scraping process can proceed seamlessly, regardless of whether the cookie consent banner is shown.

tryCatch({
  #Try to find the button
  rechazar <- remDr$findElement(using = "xpath", "//button[@id='onetrust-reject-all-handler']")
  
  # Click if finds it
  rechazar$clickElement()
  message("Cookie banner found and rejected.")
}, error = function(e) {
  
  # Do nothing if it does not find it
  message("No cookie banner detected. Continuing execution.")
})

Scrapbook

Function

The scrap_books function is designed to scrape book information from the Casa del Libro website. It first navigates to the pre-defined category and subcategory using XPath selectors. Once on the page, it collects details such as book titles, authors, and image URLs from the selected number of pages (in our case, 10 pages) using a combination of CSS selectors and HTML parsing.

For each page, the function extracts the relevant book information, storing it in separate lists for titles, authors, and image URLs. After gathering the data, it attempts to navigate to the next page, clicking the pagination button. If the button is not found, it stops the process.

Finally, the function compiles the extracted data into a dataframe that includes the category name, book titles, authors, and image URLs, which is then returned for further analysis or processing.

# Scrapbook function
scrap_books <- function(category_xpath, subcategory_xpath, category_name, num_pages = 10) {
  
  # Click in category
  enlace <- remDr$findElement(using = "xpath", category_xpath)
  enlace$clickElement()
  
  Sys.sleep(5)
  
  # Click in subcategory
  enlace <- remDr$findElement(using = "xpath", subcategory_xpath)
  enlace$clickElement()
  
  # Create empty lists to store data
  all_titles <- c()
  all_authors <- c()
  all_img_urls <- c()

  # Extract data in n pages
  for (i in 1:num_pages) {
    
    Sys.sleep(5)  
    
    # Extract element of the book
    product_elems <- remDr$findElements(using = "css selector", "div.compact-product")
    product_html <- sapply(product_elems, function(x) x$getElementAttribute("outerHTML")[[1]])
    parsed_products <- lapply(product_html, read_html)
    
    # Titles
    titles <- sapply(parsed_products, function(x) x |> 
                       html_node("a.product-title") |> 
                       html_text())
    
    # Authors
    authors <- sapply(parsed_products, function(x) {
      author_node <- x |> 
        html_node("p.truncate-text")
      if (!is.null(author_node)) html_text(author_node) else NA
    })
    
    # Images' url
    img_urls <- sapply(parsed_products, function(x) {
      x %>% html_node("img") |> 
        html_attr("src")
    })
    
    # Save in empty lists
    all_titles <- c(all_titles, titles)
    all_authors <- c(all_authors, authors)
    all_img_urls <- c(all_img_urls, img_urls)
    
    # Go to next page
    boton_pagina <- tryCatch({
      remDr$findElement(using = "xpath", paste0("//button[normalize-space(text())='", i + 1, "']"))
    }, error = function(e) NULL)
    
    if (!is.null(boton_pagina)) {
      boton_pagina$clickElement()
      Sys.sleep(5)  
      remDr$refresh() 
    } else {
      message("Button ", i + 1, "not found.")
      break
    }
  }
  
  # Create dataframe with the data
  result <- data.frame(
    category = category_name,
    title = all_titles,
    author = all_authors,
    img_url = all_img_urls,
    stringsAsFactors = FALSE
  )
  
  return(result)
}

Application

Once the function is defined, we apply it to scrape the data.

# Apply function in each category & subcategory
temp_casa.libro <- list()

for (i in 1:nrow(categories)) {
  books <- scrap_books(
    category_xpath = categories$category_xpath[i],
    subcategory_xpath = categories$subcategory_xpath[i],
    category_name = categories$category[i]
  )
  
  temp_casa.libro[[categories$category[i]]] <- books
}

Save results

Results are then save in a dataframe for further analysis.

#save results
casa.libro <- do.call(rbind, temp_casa.libro)

End selenium

Finally, we close Selenium after the scrapping process is complete. This ensures that the browser window opened by Selenium is properly closed, freeing up system resources and maintaining a clean environment for subsequent tasks.

#end selenium
remDr$close()

Goodreads

Goodreads is an online platform that connects readers worldwide. It offers features such as book reviews, ratings, and personalized recommendations, fostering a vibrant community for book enthusiasts. This includes the Goodreads Choice Awards, annual recognitions of the year’s most popular books across various genres that are chosen by the platform’s users.

For this research, we scraped the results of the 2024 awards, which established a record-breaking number of user votes (6.2 million). The selected categories to match as close as possible Casa del Libro were the following:

• Mystery and Thriller books
• Romance books
• Science Fiction books
• History and Biography books
• Young adult fantasy books

Scrapping

The first step is to define the URLs that need to be scrapped and create an empty dataframe where the data will be stored.

Then, a loop is created to go through each URL, retrieving the book titles, cover image URLs, and category names. The extracted category name is cleaned by removing the prefix “Readers’ Favorite” and book details are stored in a temporary data frame, which is then appended to the main Goodreads data frame.

url <- "https://www.goodreads.com/choiceawards/readers-favorite-mystery-thriller-books-2024"
url2 <- "https://www.goodreads.com/choiceawards/readers-favorite-romance-books-2024"
url3 <- "https://www.goodreads.com/choiceawards/readers-favorite-ya-fantasy-books-2024"
url4 <- "https://www.goodreads.com/choiceawards/readers-favorite-science-fiction-books-2024"
url5 <- "https://www.goodreads.com/choiceawards/readers-favorite-history-bio-books-2024"
urls <- c(url, url2, url3, url4, url5)

goodreads <- data.frame()

## Loop to extract the data
for (url in urls) {
    page <- read_html(url)
    
    # Extract book titles
    titles <- page %>%
        html_nodes(".pollAnswer__bookLink img") %>% 
        html_attr("alt")
    
    # Extract cover images
    covers <- page %>%
        html_nodes(".pollAnswer__bookLink img") %>% 
        html_attr("src")
    
    # Extract category from the HTML
    category <- page %>%
        html_element(".gcaMastheader") %>%  
        html_text() %>%  
        gsub("Readers' Favorite ", "", .) 
    
    # Create a temporary data frame
    temp_books <- data.frame(title = titles, img_url = covers, category = category, stringsAsFactors = FALSE)
    
    # Append to the main data frame
    goodreads <- bind_rows(goodreads, temp_books)
}

After collecting the book information, we extracted the book title and author separately using regular expressions.

# Use general expression to separate title and author
goodreads <- goodreads |> 
  rowwise() |> 
  mutate(author = str_extract(title, "(?<= by ).*"),
        title = str_extract(title, ".*(?= by )"))

Join data

Once both webpages are harvested, we combined both data into a single dataset and added a webpage variable to identify from which source is each book from.

In addition, we recoded the category “History & Biography” from Goodreads to simply “Biography” for consistency.

best_books <- bind_rows(
  goodreads |> 
    mutate(webpage = "Goodreads", category = recode(category, "History & Biography" = "Biography")),
  casa.libro |> 
    mutate(webpage = "Casa del libro")
  )   

write_xlsx(best_books, "best_books.xlsx")

Extract colors

Function

Once we have our final dataset, we defined a new function extract_colors to obtain color palettes from the book cover images given its URL. It first reads the image from the provided URL and resizes to reduce processing time. The image is then converted into pixel data. If the image data is in bitmap format (type of image representation where the image is made up of a grid of individual pixels, each of which has its own color value), it is normalized to a numeric range from 0 to 1 by dividing the pixel values by 255. The pixel data is then converted into a data frame containing the RGB channels (red, green, blue) for each pixel.

then, the function applies k-means clustering to the RGB data to identify the most dominant colors in the image. Finally, it returns the RGB values of the dominant colors identified by the clustering process in a vector format that represents the colors.

# Defined function
extract_colors <- function(image_url, n_colors = 6) {
  
  # read image from url
  img <- image_read(image_url)  |> 
    image_resize("50x50") # Resize to reduce processing time
  
  # Convert the image to pixel data
  img_data <- image_data(img)
  
  # Check if the structure is a bitmap
  if (inherits(img_data, "bitmap")) {
    # If it is a bitmap, we convert it to a numeric format
    img_data <- as.integer(img_data) / 255
  }
  
  # Convert the image to a data frame
  # Extract the RGB channels
  df <- data.frame(
    red = as.vector(img_data[,,1]),    # Red
    green = as.vector(img_data[,,2]),  # Green
    blue = as.vector(img_data[,,3])    # Blue
  )
  
  # Apply k-means to find dominant colors
  kmeans_result <- kmeans(df, centers = n_colors, nstart = 25, iter.max = 100)
  
  # Convert dominant colors to RGB format
  dominant_colors <- rgb(kmeans_result$centers[,1],
                         kmeans_result$centers[,2],
                         kmeans_result$centers[,3], maxColorValue = 1)
  
  # Return colors as a vector of colors 
  return(dominant_colors)
}

Application

The function is then applied to our dataset

best_books$colors <- sapply(best_books$img_url, extract_colors, simplify = FALSE)

Visualization

Finally, we visualized the extracted colors by displaying a palette derived from the first book’s cover to ensure the previous steps worked correctly.

show_col(best_books$colors[[1]])

Analysis

Tidy data

To begin our analysis, each book’s color palette (stored as a list) is broken down into individual rows, making each color its own observation allowing for more detailed color analysis.

# Expand the dataset to individual rows for each color
best_books_expanded <- best_books |> 
  unnest(colors)

write_xlsx(best_books_expanded, "best_books_expanded.xlsx")

Hex to RGB

Once the colors are unnested, we converted hex color codes into RGB values to extract red, green, and blue components.

# Function to convert hex colors to RGB
hex_to_rgb <- function(hex) {
  rgb_matrix <- col2rgb(hex) / 255  
  data.frame(hex = hex, R = rgb_matrix[1, ], G = rgb_matrix[2, ], B = rgb_matrix[3, ])
}

# Convert colors from hex to RGB
color_data <- best_books_expanded |> 
  mutate(rgb_values = map(colors, hex_to_rgb)) |> 
  unnest(rgb_values) 

Clusters

Then, we applied k-means clustering to group colors into six clusters per book category. If a category has fewer than six colors, unique clusters are assigned to each row; otherwise, k-means clustering organizes colors based on their RGB values. This approach helps identify dominant color trends within different book genres.

# k-means clustering to group colors into 6 clusters
set.seed(123) 

color_data_clustered <- color_data |> 
  group_by(category) |> 
  group_modify(~ {
    if (nrow(.x) < 6) {
      .x$cluster <- as.factor(seq_len(nrow(.x)))  
    } else {
      kmeans_result <- kmeans(.x[, c("R", "G", "B")], centers = 6)
      .x$cluster <- as.factor(kmeans_result$cluster)
    }
    return(.x)
  }) |> 
  ungroup()

Cluster representative

To refine the previous clustering step, we chose to identify representative color for each cluster. This provides a simplified way to analyze dominant colors within each cluster.

# Map clusters to representative colors 
cluster_representative <- color_data_clustered |> 
  group_by(category, cluster) |> 
  summarise(representative_color = first(hex))  

# Pick first as representative

Map clusters

Following the cluster simplification, we joined the clusters and representative colors to the expanded dataset.

# Replace original colors with their cluster representative
best_books_clustered <- best_books_expanded |> 
    full_join(color_data_clustered, by = c("colors", "category", "title", "author", "webpage")) |> 
    full_join(cluster_representative, by = c("category", "cluster"))

Representation of colors per category

Afterwards, we calculate the frequency of each color within each category, rank them in descending order, and select the six most common colors per category.

# Count occurrences of grouped colors per category
color_counts <- best_books_clustered |> 
  group_by(category, representative_color) |> 
  summarise(Freq = n(), .groups = "drop") |> 
  arrange(category, desc(Freq))

top_colors_all <- color_counts |> 
  group_by(category) |> 
  arrange(desc(Freq)) |> 
  slice_head(n = 6) |> 
  mutate(position = row_number()) |> 
  ungroup()

Color representation per category

# Ensure text color contrasts with background
top_colors_all <- top_colors_all |> 
  mutate(text_color = ifelse(
    (col2rgb(representative_color)[1,] * 0.299 +
     col2rgb(representative_color)[2,] * 0.587 +
     col2rgb(representative_color)[3,] * 0.114) > 150, 
    "black", "white"
  ))


ggplot(top_colors_all, aes(x = factor(position), y = category, fill = representative_color)) +
    geom_tile(color = "gray70", linewidth = 0.7) +  
    scale_fill_identity() +  
    scale_color_identity() +  
    theme_minimal(base_size = 14) +
    labs(title = "Top 6 Most Frequent Colors by Category",
         subtitle = "Most dominant colors used in book covers across different genres",
         x = NULL, y = NULL) +
   theme(
    panel.background = element_rect(fill = "transparent", color = NA),  
    plot.background = element_rect(fill = "transparent", color = NA),   
    panel.grid = element_blank(), 
    axis.text.x = element_blank(),  
    axis.ticks = element_blank(),
    strip.text = element_text(face = "bold", size = 10), 
    axis.text.y = element_text(face = "bold", size = 10),  
    plot.title = element_text(face = "bold", size = 14, hjust = 0.5),
    plot.subtitle = element_text(size = 12, hjust = 0.5)
)

Color representation per category and webpage

Furthermore, we repeated the previous analysis for each webpage, representing the color palette of each category per source. The comparative analysis reveals notable differences in color usage between the two platforms, indicating potential variations in design preferences, marketing strategies, or audience expectations.

cluster_representative <- color_data_clustered |> 
  group_by(category, cluster, webpage) |> 
  summarise(representative_color = first(hex)) 

best_books_clustered <- best_books_expanded |> 
    full_join(color_data_clustered, by = c("colors", "category", "title", "author", "webpage")) |> 
    full_join(cluster_representative, by = c("category", "cluster", "webpage"))

top_colors_all <- best_books_clustered |> 
  group_by(category, webpage, representative_color) |> 
  summarise(Freq = n(), .groups = "drop") |> 
  arrange(category, webpage, desc(Freq)) |>   # Ensure webpage is considered
  group_by(category, webpage) |> 
  slice_head(n = 6) |> 
  mutate(position = row_number()) |>  
  ungroup() |> 
  mutate(text_color = ifelse(
    grepl("^#", representative_color),  # Ensure it's a hex color
    ifelse(
      (col2rgb(representative_color)[1,] * 0.299 +
       col2rgb(representative_color)[2,] * 0.587 +
       col2rgb(representative_color)[3,] * 0.114) > 150, 
      "black", "white"
    ), 
    "black"  # Default to black if invalid color
  ))


ggplot(top_colors_all, aes(x = position, y = category, fill = representative_color)) +
    geom_tile(color = "white", size = 0.5) + 
    scale_fill_identity() +  
    scale_color_identity() +  
    theme_minimal() +
    labs(title = "Top 6 Most Frequent Colors by Category and Source", x = NULL, y = NULL) +
    theme(
        axis.text.x = element_blank(),
        axis.ticks = element_blank(),
        panel.grid = element_blank(),
        strip.text = element_text(face = "bold", size = 12),
        axis.text.y = element_text(face = "bold", size = 10)
    ) +
    facet_wrap(~webpage)  # Should separate by webpage

For example, Science Fiction on Casa del Libro features vibrant purples, blues, and neon greens, whereas in Goodreads the palette reflects subdued tones like gray and dark green. And in Romance, Casa del Libro highlights deep reds and warm neutrals, while Goodreads includes brighter blues and greens.

Top author

In addition, we identified the most frequently occurring colors in the book covers of the most prolific author in the dataset and visualized the color palette. The script first determines the top author based on the number of books, then extracts the six most dominant colors from their book covers and each color is displayed as a tile in a bar-like palette, with contrasting text labels for readability.

top_author <- best_books %>% 
    count(author, sort = TRUE) %>% 
    arrange(desc(n)) %>%
    slice(1) %>%
    pull(author) %>% 
    .[[1]]  # Force extraction of only the first element


top_authors_colors <- best_books %>% 
    filter(author == top_author) %>% 
    unnest_longer(colors) %>% 
    count(author, colors, sort = TRUE)  %>% 
    slice_head(n = 6) %>% 
    mutate(rank = row_number())  %>% 
    ungroup()  %>%
  mutate(text_color = ifelse(
    grepl("^#", colors),  # Ensure it's a hex color
    ifelse(
      (col2rgb(colors)[1,] * 0.299 +
       col2rgb(colors)[2,] * 0.587 +
       col2rgb(colors)[3,] * 0.114) > 150, 
      "black", "white"
    ), 
    "black"  # Default to black if invalid color
  ))

ggplot(top_authors_colors, aes(x = rank, y = reorder(author, -n), fill = colors)) +
    geom_tile(color = "white", size = 0.5) + 
    geom_text(aes(label = colors, color = text_color), size = 3, fontface = "bold") +  
    scale_fill_identity() +  
    scale_color_identity() +   
    theme_minimal() +
    labs(title = "Top Author Palette",
         x = "", y = "") +
    theme(axis.text.x = element_blank(), 
          axis.ticks.x = element_blank(),
          panel.grid = element_blank())

Top 5 authors

For further analysis, we selected the top 5 authors in our dataset visualizing their six most frequent colors found in their book covers.

top_authors <- best_books  %>%
    count(author, sort = TRUE) %>% 
    arrange(desc(n))  %>% 
    slice(1)  %>%  
    pull(author)  %>%   
    head(5)  # Keep only the first 5 elements


top_authors_colors <- best_books  %>% 
    filter(author %in% top_authors) %>% 
    unnest_longer(colors) %>%  
    count(author, category, colors, sort = TRUE) %>% 
    group_by(author, category) %>%   
    slice_head(n = 6)  %>%  
    mutate(rank = row_number()) %>%  
    ungroup() %>% 
    mutate(author_label = paste0(author, " (", category, ")")) %>% 
    mutate(text_color = ifelse(
        grepl("^#", colors),  
        ifelse(
            (col2rgb(colors)[1,] * 0.299 +
             col2rgb(colors)[2,] * 0.587 +
             col2rgb(colors)[3,] * 0.114) > 150, 
            "black", "white"
        ), 
        "black"  
    ))


ggplot(top_authors_colors, aes(x = rank, y = reorder(author_label, -n), fill = colors)) +
    geom_tile(color = "gray", size = 0.5) +  
    geom_text(aes(label = colors, color = text_color), size = 2.5, fontface = "bold") +  
    scale_fill_identity() +  
    scale_color_identity() +   
    theme_minimal() +
    labs(title = "Top 6 Colors for the 5 Most Prolific Authors",
         x = "", y = "") + 
    theme(axis.text.x = element_blank(),
          axis.ticks.x = element_blank(),
          panel.grid = element_blank())

Here it can be observed that the color palettes used by different authors show significant variation without a clear pattern.

Regarding romance authors, Julia Quinn’s covers predominantly feature soft, pastel shades, while in contrast, Megan Maxwell’s books lean towards dark and muted tones.

Similarly, science fiction author Brandon Sanderson and Philip K. Dick show different patterns, the former’s covers mix dark and neutral shades while the latter’s books stand out with a primarily white and light-colored palette.

This visualization provides insights into the color trends associated with an author’s book covers, which can be useful for design analysis and trend exploration.

Shiny app

https://mariacarda.shinyapps.io/data_harvesting_books/

To examine all the analysis and the different palettes obtained, we developed a shiny app that allows users to explore the most dominant colors used in book covers.

ui <- fluidPage(
  theme = shinytheme("flatly"),
  useShinyjs(),  
  
  titlePanel("The Bestseller Palette"),
  
  sidebarLayout(
    sidebarPanel(
      pickerInput("category_select", "Select a category:",
                  choices = c("All", unique(best_books$category)),
                  selected = "All", multiple = FALSE, 
                  options = list(`style` = "btn-primary")),
      
      pickerInput("author_select", "Select an author:",
                  choices = c("All"),
                  selected = "All", multiple = FALSE, 
                  options = list(`style` = "btn-info"))
    ),
    
    mainPanel(
      tabsetPanel(id = "selected_tab",
                  
        tabPanel("Book covers", fluidRow(uiOutput("book_images"))),
        
        tabPanel("Platform Palette", plotOutput("colorTiles")),
        
        tabPanel("Author Analysis", 
                 plotOutput("colorPlot"),
                 uiOutput("bookImages"))
      )
    )
  )
)

# Server
server <- function(input, output, session) {
  
  # Filtrar datos
  filtered_data <- reactive({
    data <- best_books
    
    if (input$category_select != "All") {
      data <- data %>% filter(category == input$category_select)
    }
    
    if (input$author_select != "All") {
      data <- data %>% filter(author == input$author_select)
    }
    
    return(data)
  })
  
  # Actualizar autores según categoría seleccionada
  observeEvent(input$category_select, {
    category_data <- filtered_data()
    
    updatePickerInput(session, "author_select", 
                      choices = c("All", unique(category_data$author)),
                      selected = "All")
  })
  
  # Renderizar imágenes de libros
  output$book_images <- renderUI({
    data <- filtered_data()
    
    if (nrow(data) == 0) {
      return(tags$p("No se encontraron libros que coincidan con los filtros seleccionados."))
    }
    
    image_elements <- lapply(1:nrow(data), function(i) {
      colors_list <- data$colors[[i]]
      if (is.list(colors_list)) {
        colors_list <- unlist(colors_list)
      }
      
      color_tiles <- tags$div(
        style = "display: flex; justify-content: center; margin-top: 5px;",
        lapply(colors_list, function(color) {
          tags$div(
            style = paste("background-color:", color, "; width: 30px; height: 30px; margin: 3px; border-radius: 3px;")
          )
        })
      )
      
      color_bar <- tags$div(
        style = paste("background: linear-gradient(to right, ", paste(colors_list, collapse = ", "), "); 
                      height: 30px; width: 200px; margin-top: 5px; border-radius: 3px;")
      )
      
      img_element <- tags$div(
        style = "text-align: center; margin: 5px;",
        tags$img(src = data$img_url[i], width = "150px", height = "225px")
      )
      
      combined_element <- tags$div(
        style = "width: 18%; max-width: 180px; display: flex; flex-direction: column; align-items: center; margin: 30px;",
        img_element,
        color_tiles,
        color_bar
      )
      
      combined_element
    })
    
    tags$div(
      style = "display: flex; flex-wrap: wrap; justify-content: center; 
               max-width: 100%; margin-left: auto; margin-right: auto; gap: 30px;",
      do.call(tagList, image_elements)
    )
  })
  
  # Platform Palette
  output$colorTiles <- renderPlot({
    color_data <- top_colors_all
    if (input$category_select != "All") {
      color_data <- color_data %>% filter(category == input$category_select)
    }
    
    ggplot(color_data, aes(x = factor(position), y = webpage, fill = representative_color)) +
      geom_tile(width = 0.95, height = 0.95) +
      scale_fill_identity() +
      theme_minimal() +
      labs(title = "",
           x = "", y = "") +
      theme(axis.text.x = element_blank(), 
            axis.ticks.x = element_blank(), 
            panel.grid = element_blank())
  })
  
  # Control de habilitación del selector de autor
  observeEvent(input$selected_tab, {
    if (input$selected_tab == "Platform Palette") {
      updatePickerInput(session, "author_select", selected = "All")
      disable("author_select")  # Deshabilitar el picker de autores
    } else if (input$selected_tab == "Author Analysis") {
      enable("author_select")  # Habilitar el picker de autores
    }
  })
  
  # Filtrar libros por autor
  filtered_books <- reactive({
    best_books %>% filter(author == input$author_select)
  })
  
  # Gráfico de colores
  output$colorPlot <- renderPlot({
    data <- filtered_books() %>%
      unnest_longer(colors) %>%
      count(author, colors, sort = TRUE) %>%
      slice_head(n = 6) %>%
      mutate(rank = row_number()) %>%
      ungroup() %>%
      mutate(text_color = ifelse(
        (col2rgb(colors)[1,] * 0.299 +
         col2rgb(colors)[2,] * 0.587 +
         col2rgb(colors)[3,] * 0.114) > 150, 
        "black", "white"
      ))

    ggplot(data, aes(x = rank, y = reorder(author, -n), fill = colors)) +
      geom_tile(color = "white", size = 0.5) +
      geom_text(aes(label = colors, color = text_color), size = 5, fontface = "bold") +
      scale_fill_identity() +
      scale_color_identity() +
      theme_minimal() +
      labs(title = "",
           x = "", y = "") +
      theme(axis.text.x = element_blank(), 
            axis.ticks.x = element_blank(),
            panel.grid = element_blank())
  })
  
  # Mostrar imágenes de libros
  output$bookImages <- renderUI({
    books <- filtered_books()
    img_tags <- lapply(books$img_url, function(url) {
      tags$img(src = url, width = "100px", style = "margin: 5px;")
    })
    do.call(tagList, img_tags)
  })
}

# Run the app
shinyApp(ui = ui, server = server)

Shiny applications not supported in static R Markdown documents

Screenshoot of the developed shiny app

Conclusions

The analysis of book cover colors across authors, genres, and platforms reveals no strict pattern but rather a dynamic interplay of artistic choices. While certain genres tend to favor specific color schemes —such as dark and cool tones for Young Adult Fantasy— there is considerable variation between platforms. Similarly, individual authors exhibit unique palettes, but there is no single defining scheme that dominates across their works.

These findings suggest that book cover design is influenced by multiple factors beyond just genre or author branding, including publisher strategies, regional aesthetics, and reader preferences. The divergence in colors across platforms further implies that book marketing is tailored to different audiences, with some covers emphasizing vibrancy while others adopt more sophisticated tones. Ultimately, it can be concluded that visual identity is shaped by both artistic vision and market-driven considerations.